A Python package with command-line utilities and scripts to aid the development of machine learning models for Silicon Lab's embedded platforms
Home Page: https://siliconlabs.github.io/mltk/
License: Other
The URLs pointing to the .zip files are out-of-date in the below YAML
mltk\utils\firmware_apps\download_urls.yaml
To reproduce the error
mltk profile keyword_spotting.mltk.zip --estimates --full-summary --device
Error Log:
Time: 2023-08-24 12:09:49
Command-line: profile keyword_spotting.mltk.zip --estimates --full-summary --device
Python version: 3.10.0 (tags/v3.10.0:b494f59, Oct 4 2021, 19:00:18) [MSC v.1929 64 bit (AMD64)]
Python path: D:\ws\mltk\.venv\Scripts\python.exe
Platform: Windows-10-10.0.22621-SP0
MLTK version: 0.18.0
MLTK repo hash: fa2876e1237519e9304407ea4424a88be5e288aa
Extracting keyword_spotting.tflite -> C:/Users/shari/AppData/Local/Temp/shari/mltk/models/keyword_spotting/extracted_files
Up-to-date: https://github.com/SiliconLabs/mltk_assets/raw/master/tools/commander/Commander_win32_x64_1v12p0b1057.zip -> C:/Users/shari/.mltk/tools/commander/v1.12
Programming ML model to device ...
Downloading https://github.com/SiliconLabs/mltk_assets/raw/master/applications/mltk_model_profiler/mltk_model_profiler-brd2601-none-0da859ec.zip
to C:/Users/shari/.mltk/downloads/mltk_model_profiler-brd2601-none-0da859ec.zip
(This may take awhile, please be patient ...)
Failed to profile model
Traceback (most recent call last):
File "d:\ws\mltk\mltk\cli\profile_mltk_cli.py", line 114, in profile_model_command
profiling_report = profile_model(
File "d:\ws\mltk\mltk\core\profile_model.py", line 77, in profile_model
profiling_model_results = profile_model_on_device(
File "d:\ws\mltk\mltk\core\profile_model.py", line 210, in profile_model_on_device
firmware_apps.program_image_with_model(
File "d:\ws\mltk\mltk\utils\firmware_apps\__init__.py", line 122, in program_image_with_model
firmware_image_path = get_image(
File "d:\ws\mltk\mltk\utils\firmware_apps\__init__.py", line 92, in get_image
download_dir = download_verify_extract(
File "d:\ws\mltk\mltk\utils\archive_downloader.py", line 129, in download_verify_extract
download_url(
File "d:\ws\mltk\mltk\utils\archive_downloader.py", line 331, in download_url
tmp_filepath, _ = urllib.request.urlretrieve(url, tmp_filepath, t.update_chunk)
File "C:\Users\shari\AppData\Local\Programs\Python\Python310\lib\urllib\request.py", line 241, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "C:\Users\shari\AppData\Local\Programs\Python\Python310\lib\urllib\request.py", line 216, in urlopen
return opener.open(url, data, timeout)
File "C:\Users\shari\AppData\Local\Programs\Python\Python310\lib\urllib\request.py", line 525, in open
response = meth(req, response)
File "C:\Users\shari\AppData\Local\Programs\Python\Python310\lib\urllib\request.py", line 634, in http_response
response = self.parent.error(
File "C:\Users\shari\AppData\Local\Programs\Python\Python310\lib\urllib\request.py", line 563, in error
return self._call_chain(*args)
File "C:\Users\shari\AppData\Local\Programs\Python\Python310\lib\urllib\request.py", line 496, in _call_chain
result = func(*args)
File "C:\Users\shari\AppData\Local\Programs\Python\Python310\lib\urllib\request.py", line 643, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 404: Not Found
Failed to profile model, err: Failed to download: https://github.com/SiliconLabs/mltk_assets/raw/master/applications/mltk_model_profiler/mltk_model_profiler-brd2601-none-0da859ec.zip
Hi,
The MLTK Model Profiler Utility tool can not be download now.
I checked the the link, it is to dropbox, and it is unavaliable now (error code 400).
Could you check this the fix it?
Thanks a lot!
Hello, I use MLTK library for profiling my tflite model and so far it has been really useful for me. I was wondering about the calculation of energy per inference metric.
In the documentation (https://siliconlabs.github.io/mltk/docs/guides/model_profiler.html), it says that,
"Estimates required energy per inference
NOTE: Estimates are provided based on the ARM Cortex-M33."
I understand that estimations are based on Cortex-M33, but it is not clear to me how is it calculated.
Is there any specific formula for the calculation?
Hi,
I am facing below error while doing this conversion for y yolov7-w6 based model file-
`raise e.with_traceback(filtered_tb) from None
ValueError: Exception encountered when calling layer "tf.concat_6" (type TFOpLambda).
Dimension 1 in both shapes must be equal, but are 48 and 52. Shapes are [1,48,18] and [1,52,22]. for '{{node tf.concat_6/concat}} = ConcatV2[N=4, T=DT_FLOAT, Tidx=DT_INT32](Placeholder, Placeholder_1, Placeholder_2, Placeholder_3, tf.concat_6/concat/axis)' with input shapes: [1,40,10,512], [1,44,14,512], [1,48,18,512], [1,52,22,512], [] and with computed input tensors: input[4] = <-1>.
Call arguments received by layer "tf.concat_6" (type TFOpLambda):
• values=['tf.Tensor(shape=(1, 40, 10, 512), dtype=float32)', 'tf.Tensor(shape=(1, 44, 14, 512), dtype=float32)', 'tf.Tensor(shape=(1, 48, 18, 512), dtype=float32)', 'tf.Tensor(shape=(1, 52, 22, 512), dtype=float32)']
• axis=-1
• name=concat`
Please help to resolve this issue.
Hi, thanks for putting this library together. I noticed the file view_model.py attempts to install a package.
install_pip_package('netron')
I would expect the library dependencies to be filled when installing the mltk package not to occur during the library runtime.
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